Generalized predictive information criteria for the analysis of feature events

Mike K.P. So, Tomohiro Ando

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This paper develops two weighted measures for model selection by generalizing the Kullback-Leibler divergence measure. The concept of a model selection process that takes into account the special features of the underlying model is introduced using weighted measures. New informa- tion criteria are defined using the bias correction of an expected weighted loglikelihood estimator. Using weight functions that match the features of interest in the underlying statistical models, the new information criteria are applied to simulated studies of spline regression and copula model selection. Real data applications are also given for predicting the incidence of disease and for quantile modeling of environmental data.

Original languageEnglish
Pages (from-to)742-762
Number of pages21
JournalElectronic Journal of Statistics
Volume7
Issue number1
DOIs
Publication statusPublished - 2013 Oct 8

Keywords

  • Feature matching
  • Information criteria
  • Model selection
  • Weighted Kullback-Leibler measure

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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